Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation
Larsson, Linnéa LU ; Nyström, Marcus LU
; Ardö, Håkan
LU
; Åström, Karl
LU
and Stridh, Martin
LU
(2016)
In Journal of Vision
16(15).
- Abstract
An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eyetracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The... (More)
An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eyetracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The binocular algorithm detects 98% of fixations in image stimuli compared to 95% when only one eye is used, while for video stimuli, both the binocular and monocular algorithms detect around 40% of smooth pursuit movements. The present article shows that using binocular information for discrimination of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithm's ability to detect smooth pursuit movements in video and movingdot stimuli. With an automated evaluation strategy, time-consuming manual annotations are avoided and a larger amount of data can be used in the evaluation process.
(Less)
- author
- Larsson, Linnéa
LU
; Nyström, Marcus
LU
; Ardö, Håkan
LU
; Åström, Karl
LU
and Stridh, Martin
LU
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Eye tracking, Signal processing, Smooth pursuit
- in
- Journal of Vision
- volume
- 16
- issue
- 15
- article number
- 20
- publisher
- Association for Research in Vision and Ophthalmology Inc.
- external identifiers
-
- scopus:85010404640
- scopus:85010404640
- pmid:28006070
- wos:000392947700020
- ISSN
- 1534-7362
- DOI
- 10.1167/16.15.20
- language
- English
- LU publication?
- yes
- id
- fb6df82b-965b-436b-9c20-38fa6d2bd6a2
- date added to LUP
- 2016-09-21 18:53:36
- date last changed
- 2024-01-19 09:40:12
@article{fb6df82b-965b-436b-9c20-38fa6d2bd6a2,
abstract = {{<p>An increasing number of researchers record binocular eye-tracking signals from participants viewing moving stimuli, but the majority of event-detection algorithms are monocular and do not consider smooth pursuit movements. The purposes of the present study are to develop an algorithm that discriminates between fixations and smooth pursuit movements in binocular eye-tracking signals and to evaluate its performance using an automated video-based strategy. The proposed algorithm uses a clustering approach that takes both spatial and temporal aspects of the binocular eyetracking signal into account, and is evaluated using a novel video-based evaluation strategy based on automatically detected moving objects in the video stimuli. The binocular algorithm detects 98% of fixations in image stimuli compared to 95% when only one eye is used, while for video stimuli, both the binocular and monocular algorithms detect around 40% of smooth pursuit movements. The present article shows that using binocular information for discrimination of fixations and smooth pursuit movements is advantageous in static stimuli, without impairing the algorithm's ability to detect smooth pursuit movements in video and movingdot stimuli. With an automated evaluation strategy, time-consuming manual annotations are avoided and a larger amount of data can be used in the evaluation process.</p>}},
author = {{Larsson, Linnéa and Nyström, Marcus and Ardö, Håkan and Åström, Karl and Stridh, Martin}},
issn = {{1534-7362}},
keywords = {{Eye tracking; Signal processing; Smooth pursuit}},
language = {{eng}},
number = {{15}},
publisher = {{Association for Research in Vision and Ophthalmology Inc.}},
series = {{Journal of Vision}},
title = {{Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation}},
url = {{http://dx.doi.org/10.1167/16.15.20}},
doi = {{10.1167/16.15.20}},
volume = {{16}},
year = {{2016}},
}